Forthcoming Articles

International Journal of Society Systems Science

International Journal of Society Systems Science (IJSSS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Society Systems Science (One paper in press)

Regular Issues

  • Exploring Prompt Engineering: Techniques, Challenges, and Use Cases   Order a copy of this article
    by Vani Eswari K. Kadhiresan, Vijayarani S 
    Abstract: Prompt engineering is an emerging area within natural language processing (NLP) that focuses on designing and refining prompts to guide large language models (LLMs) such as GPT and BERT. As LLMs have advanced in size and capability, the role of prompt engineering has become increasingly important in shaping model behaviour and improving response quality. Well-crafted prompts enhance accuracy, reduce ambiguity, and help mitigate issues such as bias and unintended outputs. The growth of few-shot and zero-shot learning has further highlighted the value of effective prompt design, enabling models to perform complex tasks with minimal training data. This paper provides an overview of the evolution, principles, and techniques of prompt engineering, explores its practical applications across various sectors, and discusses challenges related to ambiguity, ethics, and scalability. By examining advanced approaches and future research trends, the study emphasises the significance of prompt engineering in developing reliable and responsible AI systems.
    Keywords: Artificial Intelligence; Natural language processing; Prompt engineering; Machine Learning; Healthcare; Deep Learning; Content Generation.
    DOI: 10.1504/IJSSS.2025.10076899
     

 

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